A cross-age face generation method based on CGAN and LSTM

Abstract

Cross-age face generation refers to generating face images of other age groups by using images of known ages. It is widely used in public safety, entertainment, etc. As to the problem that the existing methods based on GANs only use age information as the generation condition and ignore the sequence of age information, we present a cross-age face generation method based on CGAN and LSTM. This method consists of four modules. The first module is a generator, which is used to generate face images of different age groups. The second module is a discriminator, whose main task is to determine whether the generated image is real or forged. The third module is a pre-trained ResNet, which is responsible for extracting the features of real images. Finally, LSTM provides age groups classification constraints for the generator by the sequence of age information.